Assessing the Validity of Surrogate Outcomes for ESRD
Bibliographic record
Abstract
Validation of current and promising surrogate outcomes for ESRD in randomized controlled trials (RCTs) has been limited. We conducted a systematic review and meta-analysis of RCTs to further inform the ability of surrogate outcomes for ESRD to predict the efficacy of various interventions on ESRD. MEDLINE, EMBASE, and CENTRAL (from inception through September 2013) were searched. All RCTs in adults with proteinuria, diabetes, or CKD stages 1-4 or renal transplant recipients reporting ≥10 ESRD events and a surrogate outcome (change in proteinuria or doubling of serum creatinine [DSCR]) for ESRD during a ≥1-year follow-up were included. Two reviewers abstracted trial characteristics and outcome data independently. To assess the correlation between the surrogate outcomes and ESRD, we determined the treatment effect ratio (TER), defined as the ratio of the treatment effects on ESRD and the effects on the change in surrogate outcomes. TERs close to 1 indicate greater agreement between ESRD and the surrogate, and these ratios were pooled across interventions. We identified 27 trials (97,458 participants; 4187 participants with ESRD). Seven trials reported the effects on change in proteinuria and showed consistent effects for proteinuria and ESRD (TER, 0.82; 95% confidence interval, 0.59 to 1.16), with minimal heterogeneity. Twenty trials reported on DSCR. Treatment effects on DSCR were consistent with the effects on ESRD (TER, 0.98; 95% confidence interval, 0.85 to 1.14), with moderate heterogeneity. In conclusion, DSCR is generally a good surrogate for ESRD, whereas data on proteinuria were limited. Further assessment of the surrogacy of proteinuria using prospective RCTs is warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.458 | 0.705 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".